Genome-wide association studies (GWAS) using single nucleotide polymorphisms (SNPs) have detected statistically significant and reproducible associations of common variants with common cancer phenotypes. However, most of these trait associated SNPs (TASs) are noncoding, intronic or intergenic, and of unknown functional significance. Identifying functional variants in GWAS loci illuminates disease mechanisms and may identify targets for pharmacological therapy. The current gap between statistical association and biological significance remains a critical problem. Recent studies demonstrate that retroelements L1 and Alu are significantly more polymorphic than previously appreciated; however, consequences of this impressive degree of polymorphism are mostly unknown. RIPs represent an untapped pool of human genomic variation that we expect has a role in common complex disease phenotypes such as cancer. Our long-range goal is to understand the functional impact of RIPs in cancer susceptibility loci. Objectives of this application are to evaluate the relationship between RIPs and TASs as a followup to previously published GWAS and elucidate the effects of RIPs on gene expression. We hypothesize that for a subset of GWAS signals in cancer, the risk variant tags a functional RIP common in the population that functionally impacts gene expression in the context of disease. We will ascertain RIPs in the vicinity of cancer risk loci, genotype or impute RIP genotypes in disease cohorts, and test for association between RIP genotype and disease phenotype to determine the role of RIPs in generating GWAS signals in studies of cancers. Additionally, we will functionally evaluate the effects of cancer- associated RIPs on transcript structure and abundance. We hypothesize that RIPs implicated in cancer risk by association will modulate expression of relevant genes by disrupting transcription or interacting with regulatory machinery. We will use qPCR, RT-PCR, and rapid amplification of cDNA ends (RACE) to discover disease- state specific alterations, and mechanistic studies including reporter gene assays to specifically implicate RIPs in gene expression changes. Identifying functional variants in GWAS intervals is essential to understanding the complex genetic landscape of cancer risk. For RIPs that are associated with cancer risk in GWAS intervals, we expect to elucidate their effects on transcript structure and abundance. The functional characterization of trait-associated RIPs will provide insights into the associated cancer pathologies. Collectively, this work will uncover a new source of human genomic variation underlying complex disease phenotypes. The proposed research will elucidate relationships between genomic variation, gene expression, and cancer risk.